Spatial ecology of perceived predation risk and vigilance behavior in white-faced capuchins
Bibliographic record
Abstract
Although predation has likely played a central role in the evolution of primate socioecology, we currently lack a thorough understanding of how fine-scale variation in perceived predation risk affects primates’ short-term space use patterns and predator avoidance strategies. We examined the spatial and ecological characteristics of predator encounters, as well as behavioral responses to perceived predation risk, in 5 groups of wild white-faced capuchins (Cebus capucinus) in Costa Rica over a 1.5-year period. Alarm-calling bouts directed at birds were more likely to originate in high forest strata, whereas alarm-calling bouts at snakes and terrestrial quadrupeds were more likely to originate near the ground. Relative risk maps based on the locations of predator encounters revealed that high-risk areas for birds and for all guilds combined consisted of more mature forest, whereas low-risk areas for these predators consisted of relatively younger forest. The animals were most vigilant near the ground, which may reflect greater perceived exposure to snakes and terrestrial predators in lower vertical levels. Incorporating the combined risk function into a predictive model of vigilance behavior improved prediction relative to null models of uniform risk or habitat-specific risk. Our results suggest that capuchin monkeys in this study system perceive reduced predation risk in the high and middle forest layers, and they adjust their vigilance behavior to small-scale spatial variation in perceived risk.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".